Definition
Artefacts Composability Resolution (ACR) is a feature of the OrionBelt Semantic Layer (OBSL) engine that answers a single, practical question while you build a query: given what you have selected so far, which other artefacts can you still add and get a valid result?
In one sentence: you compose meaning by name, and ACR guarantees the engine can hold it together — every offered artefact is known to compile.
Why Composability Needs Resolving
A semantic model is a graph of data objects connected by joins, with dimensions, measures, and metrics defined on top. Not every combination of these is valid: combining the wrong artefacts can multiply rows (fanout) or simply have no join path at all. Without help, a person — or an AI agent — has to understand the physical joins underneath just to know what is safe to ask for.
How ACR Works
ACR walks the model's join graph from your current selection (the anchor) and resolves the exact set of artefacts that remain composable with it:
- Dimensions you can group by, reachable through fanout-safe joins.
- Measures and metrics you can aggregate, drawn from the facts your selection belongs to.
- Cross-fact measures still combinable through the Composite Fact Layer (CFL) — OBSL's
UNION ALLplanner for independent fact tables — surfaced separately so you know they join at a higher level.
The result is the composable set: a precise, deterministic list, not a guess. Because ACR is driven by the same join logic the compiler uses, anything it offers is guaranteed to compile.
Two Consumers, One Mechanism
For people: a guided query builder
The dimension, measure, and metric pickers highlight what fits your current query, so you compose valid analyses without needing to understand the physical joins underneath. Dead-end selections simply never appear.
For AI agents and BI tools: safe artefact composition
ACR turns query construction into safe artefact composition. The agent selects from named artefacts that are known to combine, instead of reasoning about table relationships and risking invalid SQL. Composition replaces guesswork.
The promise it keeps: in a semantic layer you compose meaning by name — and ACR is what guarantees the names you combine actually hold together at the engine level.
Related OrionBelt Concepts
- Composite Fact Layer (CFL) is where cross-fact measures from the composable set actually recombine.
- Semantic Sidecar is the pattern that exposes ACR-driven composition to every consumer through one governed API.
- Governed Text-to-SQL benefits from ACR by constraining agents to artefacts that are known to combine.
Frequently Asked Questions
What is Artefacts Composability Resolution (ACR)?
A feature of the OrionBelt Semantic Layer engine that answers, while you build a query, which other artefacts (dimensions, measures, metrics) can still be added to the current selection and produce a valid result. Starting from an anchor selection, it walks the model's join graph and returns the exact composable set.
What does the composable set contain?
Dimensions you can group by (reachable through fanout-safe joins), measures and metrics you can aggregate (from the facts your selection belongs to), and cross-fact measures still combinable through the Composite Fact Layer (CFL), surfaced separately.
Why is ACR guaranteed to compile?
Because ACR is driven by the same join logic the compiler uses. The composable set is a precise, deterministic list, not a guess, so anything it offers is known to produce valid SQL.
How do AI agents use ACR?
It turns query construction into safe artefact composition: the agent selects from named artefacts known to combine, instead of reasoning about table relationships and risking invalid SQL. For people, the same mechanism powers a guided query builder.